Current agent memory systems compress or discard the causal reasoning chains, emotional context, and decision justifications that make past behavior interpretable, retaining only factual summaries. This creates systematic epistemic distortion where accurate facts combine with missing rationale to produce subtly wrong conclusions that propagate forward undetected across agent generations. No production memory architecture currently supports both scalable compression and queryable decision provenance, forcing a false choice between storage efficiency and interpretability.

Agent memory systems discard reasoning chains and decision justifications during compression, causing subtle downstream errors that compound across agent generations with no way to trace or debug them.

AI engineering teams at companies running multi-agent systems in production (finance, healthcare, enterprise automation) who need to audit, debug, and explain agent behavior.

Teams already pay heavily for observability (Datadog, LangSmith) and compliance tooling; a memory layer that makes agent reasoning queryable without 10x storage costs fills a gap that's blocking production deployments in regulated industries today.

MVP: a structured memory store (Postgres + vector DB hybrid) with a provenance DAG that links compressed fact nodes back to full reasoning chain snapshots stored in cold storage, exposed via a query API that resolves 'why did the agent conclude X?' — start with LangChain/CrewAI integrations.

Subset of the $30B+ observability market intersecting the $5B+ AI infrastructure market; initial wedge is the ~50K teams running agents in production today, expanding as agent deployments become standard.